You are a PM at YouTube. You released a new feature. It doesn't matter what feature was released. After the release, number of mobile video views went up by 10%, and number of desktop comments under videos went down by 20%. What would you do?

Introduction

The recent feature release on YouTube has led to an unexpected divergence in user behavior across platforms. While mobile video views increased by 10%, desktop comments decreased by 20%. This situation requires a thorough analysis to understand the root cause and develop appropriate solutions.

I'll approach this issue systematically, starting with clarifying questions, then moving through external factor analysis, product understanding, metric breakdown, data gathering, hypothesis formation, root cause analysis, and finally, validation and resolution planning.

Step 1

Clarifying Questions (3 minutes)

• What specific feature was released, and what was its intended purpose?

• Over what time frame were these changes observed?

• Are there any notable differences in the impact across user segments or regions?

• Have there been any recent changes to the comment system or mobile app interface?

• Are there any ongoing A/B tests that might be affecting these metrics?

• Have we observed any changes in other related metrics, such as watch time or user engagement?

Why this matters: Understanding the feature's purpose and the context of the changes will help us narrow down potential causes and impacts.

Hypothetical answer: The feature was a new recommendation algorithm aimed at improving content discovery, implemented two weeks ago. The changes were observed across all regions, with a slightly higher impact on younger users.

Impact on approach: This information would guide us to focus on how the new algorithm might be affecting user behavior differently on mobile and desktop platforms.